23 papers
Kronecker-Structured Nonparametric Spatiotemporal Point Processes
Zhitong Xu, Qiwei Yuan, Yinghao Chen +3
Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges. Classical…
A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
Shridhar Vashishtha, Krishna Prasath Logakannan, Jacob Hochhalter +2
Digital twins are developed to model the behavior of a specific physical asset (or twin), and they can consist of high-fidelity physics-based models or surrogates. A highly accurat…
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
Matthew Lowery, John Turnage, Zachary Morrow +4
This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kernel-based integral operators for…
Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling
Zhitong Xu, Qiwei Yuan, Yinghao Chen +2
Multi-class event streams arise in numerous real-world applications, where uncovering structured, interpretable inter-event relationships, together with accurate prediction, remain…
Dual-Agent Co-Training for Health Coaching via Implicit Adversarial Preference Optimization
Da Long, Lingyi Fu, Diya Michelle Rao +3
Motivational-interviewing-based health coaching is an effective approach for improving mental health and promoting healthy behavior change. However, the scarcity of trained human c…
Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
Keyan Chen, Qiwei Yuan, Zhitong Xu +2
Events in spatiotemporal systems are ubiquitous, yet modeling their complex distributions remains challenging. Existing point process models often rely on strong structural assumpt…